SFSDAF: an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion

Li, Xiadong; Foody, Giles M.; Boyd, Doreen S.; Ge, Yong; Zhang, Yihang; Du, Yun; Ling, Feng

Abstract

Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both fine spatial and temporal resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives fine spatial resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a fine resolution image at the time of acquisition of coarse resolution images using only one prior coarse and fine resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of fine resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics.

Citation

Li, X., Foody, G. M., Boyd, D. S., Ge, Y., Zhang, Y., Du, Y., & Ling, F. (2020). SFSDAF: an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion. Remote Sensing of Environment, 237, https://doi.org/10.1016/j.rse.2019.111537

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2019
• Online Publication Date: Nov 26, 2019
• Publication Date: 2020-02
• Deposit Date: Nov 22, 2019
• Publicly Available Date: Nov 27, 2020
• Journal: Remote Sensing of Environment
• Print ISSN: 0034-4257
• Electronic ISSN: 1879-0704
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 237
• Article Number: 111537
• DOI: https://doi.org/10.1016/j.rse.2019.111537
• Keywords: Spatio-temporal image fusion, Land cover class fraction, FSDAF
• Public URL: https://nottingham-repository.worktribe.com/output/3354550
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0034425719305565
• Additional Information: This article is maintained by: Elsevier; Article Title: SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion; Journal Title: Remote Sensing of Environment; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.rse.2019.111537; Content Type: article; Copyright: © 2019 Elsevier Inc. All rights reserved.